We show that deep autoregressive generative models are ideally suitable for solving various computational imaging problems for the following reasons. First, it explicitly models the distribution of each pixel in relation to its causal neighbor. Thus, when used as an image prior, this explicit pixel dependency modeling helps it to better reconstruct low level details without artifacts (see Figure REF ). Second, this framework gives us an explicit expression for the image prior, which can be used for doing MAP inference. Moreover, the entire framework is differentiable, which is amenable for gradient based inference. Third, its ability to capture long range dependencies in images makes them ideal for handling global multiplexing in compressive imaging setups.
Given these advantages with deep autoregressive models, we use it for solving various computational imaging problems such as - Single Pixel Camera (SPC) [1]}, Line Sensor (LiSens) [2]} and lensless imaging - FlatCam [3]}. Our results demonstrate that we perform better than the current state-of-the-art methods in both traditional and learning based approaches.
